Soil organic matter estimation in precision agriculture using wireless sensor networks

Nguyen, L; Kodagoda, S

Soil organic matter estimation in precision agriculture using wireless sensor networks

Nguyen, L

Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017
Issue Date:: 2017-01-31

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Field	Value	Language
dc.contributor.author	Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.available	2016-08-01	en_US
dc.date.issued	2017-01-31	en_US
dc.identifier.citation	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017	en_US
dc.identifier.isbn	9781509035496	en_US
dc.identifier.uri	http://hdl.handle.net/10453/67464
dc.description.abstract	© 2016 IEEE. In order to achieve the ever increasing quantity and quality demands for agricultural products, technological innovations must be explored. In most cases, low cost and high quality sensing options are a top priority. This paper addresses the problem of predicting soil organic matter content in an agriculture field using information collected by a low-cost network of mobile, wireless and noisy sensors that can take discrete measurements in the environment. In this context, it is proposed that the spatial phenomenon of organic matter in soil to be monitored is modeled using Gaussian processes. The proposed model then enables the wireless sensor network to estimate the soil organic matter field at all unobserved locations of interest. The estimated values at predicted locations are highly comparable to those at corresponding points on a realistic image that is aerially taken by a very expensive and complex remote sensing system.	en_US
dc.relation.ispartof	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016	en_US
dc.relation.isbasedon	10.1109/ICARCV.2016.7838635	en_US
dc.title	Soil organic matter estimation in precision agriculture using wireless sensor networks	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In order to achieve the ever increasing quantity and quality demands for agricultural products, technological innovations must be explored. In most cases, low cost and high quality sensing options are a top priority. This paper addresses the problem of predicting soil organic matter content in an agriculture field using information collected by a low-cost network of mobile, wireless and noisy sensors that can take discrete measurements in the environment. In this context, it is proposed that the spatial phenomenon of organic matter in soil to be monitored is modeled using Gaussian processes. The proposed model then enables the wireless sensor network to estimate the soil organic matter field at all unobserved locations of interest. The estimated values at predicted locations are highly comparable to those at corresponding points on a realistic image that is aerially taken by a very expensive and complex remote sensing system.

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http://hdl.handle.net/10453/67464